PhD Candidate in AI-assisted medical imaging with MRI
Develop advanced neural network models to improve MRI image reconstruction and compensate for magnetic field disturbances.
Best for:Candidates with a strong background in physics, mathematics, or computer science and a keen interest in medical imaging and machine learning.
Overview
The Department of Physics at the Norwegian University of Science and Technology (NTNU) is seeking a PhD candidate to join the MRI Physics Group. This interdisciplinary project focuses on utilizing Implicit Neural Representations (INR) to reconstruct high-quality MR images while mitigating signal loss and geometric distortions caused by magnetic field inhomogeneities. The research will primarily involve functional MRI data of the spinal cord acquired from a 7 Tesla system, with potential applications extending to industrial geology. The successful candidate will contribute to a collaborative research environment, working alongside experts in MRI and X-ray physics to advance diagnostic and neuroscience capabilities.
What you'll do
- Simulate MRI data and model physical disturbances.
- Develop, implement, and train Implicit Neural Representation (INR) models for MRI time-series data.
- Analyze functional MRI data of the spinal cord.
- Disseminate research findings through academic publications.
- Present research at international conferences.
- Assist with departmental teaching activities.
Requirements
Degree required: Master's degree in physics, mathematics, biomedical engineering, computer science, or equivalent.
Degree offered: PhD
Language: English (fluent); Norwegian/Scandinavian is an advantage.
Instruction: English
- Strong academic background with an average grade of B or better.
- Proficiency in programming.
- Excellent oral and written communication skills in English.
- Ability to work independently and collaboratively in an interdisciplinary team.
- Must meet admission requirements for the PhD programme in Physics, Biophysics, or Medical Technology at NTNU.
- Master's degree must be completed by August 31, 2027.
- Must be able to be physically present at the institution regularly.
Funding & terms
How to apply
Deadline: 2026-10-18
Applications must be submitted electronically via the Jobbnorge portal. Ensure all required documentation is attached, as the assessment is based solely on the information provided by the deadline.
- CV
- Bachelor's and Master's transcripts and diplomas
- Copy of Master's thesis or draft
- Letter of motivation
- Publications and research work
- Contact information for two referees
Contact
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